Age classification of humans based on image depth and human pose

ABSTRACT

A mechanism is described for facilitating age classification of humans using image depth and human pose according to one embodiment. A method of embodiments, as described herein, includes facilitating, by one or more cameras of a computing device, capturing of a video stream of a scene having persons, and computing overall-depth torso lengths of the persons based on depth torso lengths of the persons. The method may further include comparing the overall-depth torso lengths with a predetermined threshold value representing a separation age between adults and children, and classifying a first set of the persons as adults if a first set of the overall-depth torso lengths associated with the first set of persons is greater than the threshold value.

FIELD

Embodiments described herein relate generally to data processing andmore particularly to facilitate age classification of humans based onimage depth and human pose.

BACKGROUND

Conventional systems fail to classify adults from children in sceneswhere human beings of all ages are detected. Although attempts have beenmade to achieve such classification conventional techniques, most ofthese techniques are highly dependent on facial features, tend to failwhen encountering a lack of clear view (such as occlusion of face,non-frontal face views), seem unable to capture high resolution imagesof faces, or are simply too intrusive to be acceptable or even ethical.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements.

FIG. 1 illustrates a computing device hosting an age classificationmechanism according to one embodiment.

FIG. 2 illustrates the age classification mechanism of FIG. 1 accordingto one embodiment.

FIG. 3A illustrates torso foreshortening in multiple views according toone embodiment.

FIG. 3B illustrates a graph showing Depth Normalized Torso Length ofkids and adults in a scene obtained using single view cameras accordingto one embodiment.

FIG. 3C illustrates a top view of a scene containing adults and kids asviewed by multiple pairs of red green blue cameras and depth camerasaccording to one embodiment.

FIG. 3D illustrates torso length, neck, and hip center on a modelaccording to one embodiment.

FIG. 3E illustrates a graph showing a camera-based Depth NormalizedTorso Length computation using Image Torso Length and depth according toone embodiment.

FIG. 3F illustrates an unclassified scene and a classified sceneaccording to one embodiment.

FIG. 4 illustrates a method for classification of adults and kidsaccording to one embodiment.

FIG. 5 illustrates a computer device capable of supporting andimplementing one or more embodiments according to one embodiment.

FIG. 6 illustrates an embodiment of a computing environment capable ofsupporting and implementing one or more embodiments according to oneembodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth.However, embodiments, as described herein, may be practiced withoutthese specific details. In other instances, well-known circuits,structures and techniques have not been shown in detail in order not toobscure the understanding of this description.

Embodiments provide for a novel technique for classification of humanbeings detected in a scene as adults or children (also referred to askids), where, for example, such classifications may be used to provideadditional safety and security for kids in places like homes, swimmingpools, theaters, schools, public parks, shopping malls, sports arenas,etc. Similarly, this novel technique may be extended for any number andtype of applications, such as customized digital signage, customerdemographic studies in retail stores, home solutions, digitalsurveillance, autonomous driving, etc., where human analytics based onvisual data may be regarded as integral part of these solutions.

Further, for example, since kids have special needs and requireadditional safety and security than a typical adult, this noveltechnique may be used to identify and analyze them uniquely such that toprovide with additional safety in certain places like swimming pools.

It is contemplated that terms like “request”, “query”, “job”, “work”,“work item”, and “workload” may be referenced interchangeably throughoutthis document. Similarly, an “application” or “agent” may refer to orinclude a computer program, a software application, a game, aworkstation application, etc., offered through an applicationprogramming interface (API), such as a free rendering API, such as OpenGraphics Library (OpenGL®), DirectX® 11, DirectX® 12, etc., where“dispatch” may be interchangeably referred to as “work unit” or “draw”and similarly, “application” may be interchangeably referred to as“workflow” or simply “agent”. For example, a workload, such as that of athree-dimensional (3D) game, may include and issue any number and typeof “frames” where each frame may represent an image (e.g., sailboat,human face). Further, each frame may include and offer any number andtype of work units, where each work unit may represent a part (e.g.,mast of sailboat, forehead of human face) of the image (e.g., sailboat,human face) represented by its corresponding frame. However, for thesake of consistency, each item may be referenced by a single term (e.g.,“dispatch”, “agent”, etc.) throughout this document.

In some embodiments, terms like “display screen” and “display surface”may be used interchangeably referring to the visible portion of adisplay device while the rest of the display device may be embedded intoa computing device, such as a smartphone, a wearable device, etc. It iscontemplated and to be noted that embodiments are not limited to anyparticular computing device, software application, hardware component,display device, display screen or surface, protocol, standard, etc. Forexample, embodiments may be applied to and used with any number and typeof real-time applications on any number and type of computers, such asdesktops, laptops, tablet computers, smartphones, head-mounted displaysand other wearable devices, and/or the like. Further, for example,rendering scenarios for efficient performance using this novel techniquemay range from simple scenarios, such as desktop compositing, to complexscenarios, such as 3D games, augmented reality applications, etc.

It is to be noted that terms or acronyms like convolutional neuralnetwork (CNN), CNN, neural network (NN), NN, deep neural network (DNN),DNN, recurrent neural network (RNN), RNN, and/or the like, may beinterchangeably referenced throughout this document. Further, terms like“autonomous machine” or simply “machine”, “autonomous vehicle” or simply“vehicle”, “autonomous agent” or simply “agent”, “autonomous device” or“computing device”, “robot”, and/or the like, may be interchangeablyreferenced throughout this document.

FIG. 1 illustrates a computing device 100 employing an ageclassification mechanism (“age mechanism”) 110 according to oneembodiment. Computing device 100 represents a communication and dataprocessing device including or representing any number and type of smartdevices, such as (without limitation) smart command devices orintelligent personal assistants, home/office automation system, homeappliances (e.g., washing machines, television sets, etc.), mobiledevices (e.g., smartphones, tablet computers, etc.), gaming devices,handheld devices, wearable devices (e.g., smartwatches, smart bracelets,etc.), virtual reality (VR) devices, head-mounted display (HMDs),Internet of Things (IoT) devices, laptop computers, desktop computers,server computers, set-top boxes (e.g., Internet-based cable televisionset-top boxes, etc.), global positioning system (GPS)-based devices,etc.

In some embodiments, computing device 100 may include (withoutlimitation) autonomous machines or artificially intelligent agents, suchas a mechanical agents or machines, electronics agents or machines,virtual agents or machines, electro-mechanical agents or machines, etc.Examples of autonomous machines or artificially intelligent agents mayinclude (without limitation) robots, autonomous vehicles (e.g.,self-driving cars, self-flying planes, self-sailing boats, etc.),autonomous equipment (self-operating construction vehicles,self-operating medical equipment, etc.), and/or the like. Further,“autonomous vehicles” are not limed to automobiles but that they mayinclude any number and type of autonomous machines, such as robots,autonomous equipment, household autonomous devices, and/or the like, andany one or more tasks or operations relating to such autonomous machinesmay be interchangeably referenced with autonomous driving.

Further, for example, computing device 100 may include a computerplatform hosting an integrated circuit (“IC”), such as a system on achip (“SoC” or “SOC”), integrating various hardware and/or softwarecomponents of voice-enabled device 100 on a single chip.

As illustrated, in one embodiment, computing device 100 may include anynumber and type of hardware and/or software components, such as (withoutlimitation) graphics processing unit (“GPU” or simply “graphicsprocessor”) 114, graphics driver (also referred to as “GPU driver”,“graphics driver logic”, “driver logic”, user-mode driver (UMD), UMD,user-mode driver framework (UMDF), UMDF, or simply “driver”) 116,central processing unit (“CPU” or simply “application processor”) 112,memory 108, network devices, drivers, or the like, as well asinput/output (I/O) sources 104, such as touchscreens, touch panels,touch pads, virtual or regular keyboards, virtual or regular mice,ports, connectors, etc. Computing device 100 may include operatingsystem (OS) 106 serving as an interface between hardware and/or physicalresources of computing device 100 and a user.

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of computing device 100 may vary fromimplementation to implementation depending upon numerous factors, suchas price constraints, performance requirements, technologicalimprovements, or other circumstances.

Embodiments may be implemented as any or a combination of: one or moremicrochips or integrated circuits interconnected using a parentboard,hardwired logic, software stored by a memory device and executed by amicroprocessor, firmware, an application specific integrated circuit(ASIC), and/or a field programmable gate array (FPGA). The terms“logic”, “module”, “component”, “engine”, and “mechanism” may include,by way of example, software or hardware and/or a combination thereof,such as firmware.

In one embodiment, as illustrated, age mechanism 110 may be hosted byoperating system 106 in communication with I/O source(s) 104 ofcomputing device 100. In another embodiment, age mechanism 110 may behosted or facilitated by graphics driver 116. In yet another embodiment,age mechanism 110 may be hosted by or part of graphics processing unit(“GPU” or simply graphics processor”) 114 or firmware of graphicsprocessor 114. For example, age mechanism 110 may be embedded in orimplemented as part of the processing hardware of graphics processor114. Similarly, in yet another embodiment, age mechanism 110 may behosted by or part of central processing unit (“CPU” or simply“application processor”) 112. For example, age mechanism 110 may beembedded in or implemented as part of the processing hardware ofapplication processor 112.

In yet another embodiment, age mechanism 110 may be hosted by or part ofany number and type of components of computing device 100, such as aportion of age mechanism 110 may be hosted by or part of operatingsystem 116, another portion may be hosted by or part of graphicsprocessor 114, another portion may be hosted by or part of applicationprocessor 112, while one or more portions of age mechanism 110 may behosted by or part of operating system 116 and/or any number and type ofdevices of computing device 100. It is contemplated that embodiments arenot limited to any particular implementation or hosting of age mechanism110 and that one or more portions or components of age mechanism 110 maybe employed or implemented as hardware, software, or any combinationthereof, such as firmware.

Voice device 100 may host network interface(s) to provide access to anetwork, such as a LAN, a wide area network (WAN), a metropolitan areanetwork (MAN), a personal area network (PAN), Bluetooth, a cloudnetwork, a mobile network (e.g., 3^(rd) Generation (3G), 4^(th)Generation (4G), etc.), an intranet, the Internet, etc. Networkinterface(s) may include, for example, a wireless network interfacehaving antenna, which may represent one or more antenna(e). Networkinterface(s) may also include, for example, a wired network interface tocommunicate with remote devices via network cable, which may be, forexample, an Ethernet cable, a coaxial cable, a fiber optic cable, aserial cable, or a parallel cable.

Embodiments may be provided, for example, as a computer program productwhich may include one or more machine-readable media having storedthereon machine-executable instructions that, when executed by one ormore machines such as a computer, network of computers, or otherelectronic devices, may result in the one or more machines carrying outoperations in accordance with embodiments described herein. Amachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), andmagneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable ReadOnly Memories), EEPROMs (Electrically Erasable Programmable Read OnlyMemories), magnetic or optical cards, flash memory, or other type ofmedia/machine-readable medium suitable for storing machine-executableinstructions.

Moreover, embodiments may be downloaded as a computer program product,wherein the program may be transferred from a remote computer (e.g., aserver) to a requesting computer (e.g., a client) by way of one or moredata signals embodied in and/or modulated by a carrier wave or otherpropagation medium via a communication link (e.g., a modem and/ornetwork connection).

Throughout the document, term “user” may be interchangeably referred toas “viewer”, “observer”, “speaker”, “person”, “individual”, “end-user”,and/or the like. It is to be noted that throughout this document, termslike “graphics domain” may be referenced interchangeably with “graphicsprocessing unit”, “graphics processor”, or simply “GPU” and similarly,“CPU domain” or “host domain” may be referenced interchangeably with“computer processing unit”, “application processor”, or simply “CPU”.

It is to be noted that terms like “node”, “computing node”, “server”,“server device”, “cloud computer”, “cloud server”, “cloud servercomputer”, “machine”, “host machine”, “device”, “computing device”,“computer”, “computing system”, and the like, may be usedinterchangeably throughout this document. It is to be further noted thatterms like “application”, “software application”, “program”, “softwareprogram”, “package”, “software package”, and the like, may be usedinterchangeably throughout this document. Also, terms like “job”,“input”, “request”, “message”, and the like, may be used interchangeablythroughout this document.

FIG. 2 illustrates age classification mechanism 110 of FIG. 1 accordingto one embodiment. For brevity, many of the details already discussedwith reference to FIG. 1 are not repeated or discussed hereafter. In oneembodiment, age mechanism 110 may include any number and type ofcomponents, such as (without limitations): detection and capturing logic201; depth-based tiling logic (“tiling logic”) 203; tile scaling logic205; pose estimation logic 207; communication/compatibility logic 209;image torso length (ITL) computation logic 211; depth normalized torsolength (DNTL) computation logic 213; overall DNTL computation logic 215;and classification logic 217.

Computing device 100 is further shown to include user interface 219(e.g., graphical user interface (GUI)-based user interface, Web browser,cloud-based platform user interface, software application-based userinterface, other user or application programming interfaces (APIs),etc.). Computing device 100 may further include I/O source(s) 108 havingcapturing/sensing component(s) 231, such as camera(s) A 242A, B 242B, N242N (e.g., Intel® RealSense™ camera), sensors, microphone(s) 241, etc.,and output component(s) 233, such as display device(s) or simplydisplay(s) 244 (e.g., integral displays, tensor displays, projectionscreens, display screens, etc.), speaker devices(s) or simply speaker(s)243, etc.

Computing device 100 is further illustrated as having access to and/orbeing in communication with one or more database(s) 225 and/or one ormore of other computing devices over one or more communication medium(s)230 (e.g., networks such as a cloud network, a proximity network, theInternet, etc.).

In some embodiments, database(s) 225 may include one or more of storagemediums or devices, repositories, data sources, etc., having any amountand type of information, such as data, metadata, etc., relating to anynumber and type of applications, such as data and/or metadata relatingto one or more users, physical locations or areas, applicable laws,policies and/or regulations, user preferences and/or profiles, securityand/or authentication data, historical and/or preferred details, and/orthe like.

As aforementioned, computing device 100 may host I/O sources 108including capturing/sensing component(s) 231 and output component(s)233. In one embodiment, capturing/sensing component(s) 231 may include asensor array including, but not limited to, microphone(s) 241 (e.g.,ultrasound microphones), camera(s) 242A, 242B, 242N (e.g.,two-dimensional (2D) cameras, three-dimensional (3D) cameras, infrared(IR) cameras, depth-sensing cameras, etc.), capacitors, radiocomponents, radar components, scanners, and/or accelerometers, etc.Similarly, output component(s) 233 may include any number and type ofspeaker(s) 243, display device(s) 244 (e.g., screens, projectors,light-emitting diodes (LEDs)), and/or vibration motors, etc.

For example, as illustrated, capturing/sensing component(s) 231 mayinclude any number and type of microphones(s) 241, such as multiplemicrophones or a microphone array, such as ultrasound microphones,dynamic microphones, fiber optic microphones, laser microphones, etc. Itis contemplated that one or more of microphone(s) 241 serve as one ormore input devices for accepting or receiving audio inputs (such ashuman voice) into computing device 100 and converting this audio orsound into electrical signals. Similarly, it is contemplated that one ormore of camera(s) 242A, 242B, 242N serve as one or more input devicesfor detecting and capturing of image and/or videos of scenes, objects,etc., and provide the captured data as video inputs into voice-enableddevice 100.

It is contemplated that embodiments are not limited to any number ortype of microphone(s) 241, camera(s) 242A, 242B, 242N, speaker(s) 243,display(s) 244, etc. For example, as facilitated by detection andcapturing logic 201, one or more of microphone(s) 241 may be used todetect speech or sound simultaneously from multiple users or speakers,such as speaker 250. Similarly, as facilitated by detection andcapturing logic 201, one or more of camera(s) 242A, 242B, 242N may beused to capture images or videos of a geographic location (such as aroom) and its contents (e.g., furniture, electronic devices, humans,animals, plats, etc.) and form a set of images or a video stream formthe captured data for further processing by age mechanism 110 atcomputing device 100.

Similarly, as illustrated, output component(s) 233 may include anynumber and type of speaker(s) 243 to serve as output devices foroutputting or giving out audio from voice device 100 for any number ortype of reasons, such as human hearing or consumption. For example,speaker(s) 243 work the opposite of microphone(s) 241 where speaker(s)243 convert electric signals into sound.

In some embodiments, computing device 100 may be placed within or partof a geographic area or, in another embodiment, remote or far away fromthe geographic area, such as a swimming pool, a park, a home, a zoo, atheater, a room, a building, a hall, a stadium, etc., and, but camera(s)242A, 242B, 242N, which may be embedded in or in communication withcomputing device 100, are capable of capturing a scene of the geographicarea having people (such as adults and/or kids) based on the locationsand movements of camera(s) 242A, 242B, 242N and their proximity to thepeople in the scene.

As previously described, several attempts have been made to achieveclassification, but all conventional techniques have failed for onereason or another. For example, human face-based approaches extracthand-engineered or deep learning-based features from a human face andattempt to classify it, but they fail for any number of reasons, such aswhen the frontal part of the face is occluded, cameras are distant,constrained conditions have occurred, and/or the like.

Similarly, body-part ratio-based approaches are known for exploitingdifferences in face length and body weights for classification purposes.However, this conventional technique also fails in scenes containingpeople with non-standing postures, such as sitting or crouching, or evenwhen there is non-uniform scaling due to surveillance camera viewpoints.

More recently, biometric approaches have been introduced, such asallowing forced expiratory spirometry to use a set of vital metabolicmeasurements for classification purpose. However, these biometricconventional techniques are intrusive (such as the use of sensors on thehuman body) and fail to operate in a seamless manner on any arbitraryperson which essentially makes such techniques unusable andinapplicable.

It is well known that adults and kids differ in their body dimensions;particularly, with respect to their torso length, where a torso lengthmay be regarded as the difference between the neck and the center of theleft and right hips of a person. Further, an adult torso (length) isgenerally higher than that of a child, where the neck and hips arelocated through a human pose-estimation algorithm as facilitated by poseestimation logic 207.

In one embodiment, detection and capturing logic 201 may facilitate oneor more camera, such as camera A 242A, camera B 242B, and camera N 242Nto capture images (such as video or still images) of a scene (such as ata park or a swimming pool) having human beings including adults andkids. These images may then be used to detect humans in the scene asfacilitated by detection and capturing logic 201. Now, for example, dueto the perspective projection of the scene on an image plane ofcamera(s) 242, a kid standing close to camera(s) 242 and an adultstanding far away from camera(s) 242 may appear to have the same torsolength measured in image pixels. In one embodiment, this conundrum maybe resolved by simply normalizing the image torso length by using thetypical depth known of a human torso as captured from the center ofcamera (a) 242, while this DNTL may then be used for classification.

In one embodiment, as illustrated, cameras 242A, 242B, 242N may beinstalled in various locations and be in communication with computingdevice 100 and detection and capturing logic 201, while, in anotherembodiment, cameras 242A, 242B, 242C may be embedded in computing device100. Further, it is contemplated that embodiments are not limited to anynumber or type of cameras and that three cameras 242A, 242B, 242Cillustrated here are merely provided as an example for brevity, clarity,and ease of understanding.

In one embodiment, torso length is used for age classification since,for example, unlike facial features, the projected torso length of aperson at a given depth remain unaffected by body movements or humansentiments, etc., as far as the torso remains in a plane parallel to theimage plane of one or more of cameras 242A, 242B, 242C to avoid what isregarded as foreshortening of body parts, such as limbs, torso, etc. Forexample, foreshortening is a process by which certain body parts appeardisproportionately shorter compared to other body parts when projectedon the image plane of one or more of cameras 242A, 242B, 242C. Aforeshortened torso may be unsuitable for estimating the real torsolength of the torso the foreshortened torso represents.

As will be further described in this document, embodiments provide forcapturing of true torso length based on the images captured by one ormore of cameras 242A, 242B, 242C and as facilitated by one or morecomponents of age mechanism 110. For example, as illustrated withrespect to FIG. 3A, a set of three cameras A 242A, B 242B, and N 242Nmay be placed at various locations and equal distances from the centerof a human body, such as image planes 312A, 312B, and 312N correspondingto cameras 242A, 242B, and 242N, respectively, are indicated withrespect to projection 301.

In some embodiments, a camera system may be employed having an areaunder observation covered by multiple camera, such as cameras 242A-242N,with significant overlap. Further, it is contemplated that a person maybe detected by more than just one camera, such as by any combination oftwo or more of cameras 242A-242N. Further, for example, identicalpersons may be located from multiple camera views such that local depths(from each camera) and global depths (from a reference point) may becalculated. Since a single person may be detected by more than just oneof cameras 242A-242N, it is contemplated that correspondingly, more thanone torso length values may exist for that person as viewed throughthose cameras of cameras 242A-242N. Although torso length may be used asa feature to determine the age of persons, such as whether they areadult or kids, it is contemplated that in some embodiments, torso lengthmay also be used for robustly compute other dimensions of humans acrossvarious poses, actions, and/or the like.

As seen in FIG. 3A, with respect to projection 301, the torso length ofhuman torso 311 being projected on image planes 312A, 312B, and 312N maydepend upon the relative orientation of torso 311 with image planes312A, 312B, and 312N of cameras 242A, 242B, and 242N, respectively.Since, in this illustrated embodiment of FIG. 3A, torso 311 is shown aslocated in plane 312A that is parallel to camera 242A, its projectedlength is maximum with respect to camera 242A. This is the case ofminimal foreshortening.

On the other hand, with regard to projection 303, since torso 311 isnearly perpendicular to image plane 312B of camera 242B, the projectedtorso length in this case may be subject to minimum or maximumforeshortening. When the orientation of the person changes as shown inprojections 303 and 305, the projected torso length also changes acrosscameras 242B, 242N. Similarly, the maximum projected torso lengthhappens in case of camera 242B and camera 242N.

Further, when human postures change arbitrarily, torso foreshorteningeffects are determined to be minimum when torso 311 is imaged on tocamera A 242A whose image plane 312A is parallel to torso 311. Byplacing a number of cameras 242A, 242B, 242N around a scene, thiscondition may be achieved to a large extent in at least one of thecamera views (such as the best view) for every person in the scene. Forexample, for the best view, the projected torso length in image planes312A, 312B, 312N of cameras 242A, 242B, 242N is maximum where theforeshortening is minimum.

For example, in a real-world scenario, since cameras 242A, 242B, 242Nneed not be equidistant from a person, the measured torso length isneeded to be normalized by the depth of the human before comparingacross views. When the image torso length (in pixels) is normalized bythe depth of the person from the center of one or more cameras 242A,242B, 242N, then the resulting DNTL can be used an effective and arobust feature for classifying kids and adults as illustrated in graph320 of FIG. 3B. As illustrated in FIG. 3B, the DNTL obtained from asingle view for about 24 kids and adults as shown on graph 320, wherebetter results may be obtained by using multiple cameras, such ascameras 242A, 242B, 242N, to minimize the effect for foreshortening.

In some embodiments, cameras 242A, 242B, 242N may include one or more ofdepth cameras, red green blue (RGB) cameras, etc., for observing andcapturing images of a scene containing kids and adults as illustratedwith respect to FIG. 3C, where scene 340 is a top view containing adultsand kids, collectively p, as viewed by multiple pairs, J, of RGB cameras341, 342, 343, 344, 345 and depth cameras 346, 347, 348, 349, 350 ofcameras 242A, 242B, 242N. For example, depth may be obtained from activedepth cameras 346, 347, 348, 349, 350, such as Intel® RealSense™, Asus®Xtion™, or through stereoscopic cameras in each view. A sparse depth-mapof the feature points in each person may also be obtained through firstestablished multi-view correspondences and then using triangulation andbundle adjustment.

Now referring back to age mechanism 110, in one embodiment, once a videostream of a scene is captured by one or more of cameras 242A, 242B,242N, detection and capturing logic 201 may then be used to detect allthe humans in the scene by applying one or more detection techniques,such as pedestrian/person detection technique, face detection technique,etc., on the RGB video stream captured by one or more of cameras 242A,242B, 242N. For example, face detection may be useful in cases where theentire body of a person may not be visible or in matters of abnormalpostures.

Once the humans, both adults and kids, are detected or located in theRGB video stream, in one embodiment, tiling logic 203 may then betriggered for performing depth-based RGB tiling where the depth of eachhuman from the center of each of cameras 242A, 242B, 242N isapproximated as the average depth value of pixels falling within acorresponding box region in the depth video stream. Further, based ondepth values and spatial proximity of the human bounding boxes, they areclustered together as RGB tiles as facilitated by tiling logic 203.

In one embodiment, tile scaling logic 205 may then be triggered to use adeep learning-based pose estimation technique to use a deep neuralnetwork (DNN), such as a convolutional neural network (CNN) for partdetection and generation of a graphical model for part assignment. Theefficiency of part detection may be useful when the test image isbrought to the same scale as that of training images, where each depthtile is scaled by an appropriate amount to achieve this goal asfacilitated by tile scaling logic 205.

Further, in one embodiment, pose estimation logic 207 may then betriggered to perform a human two-dimensional (2D) pose estimation fordetermining pixel locations of various body parts of differentindividuals in the scene. For example, post estimation logic 207 may beused to detect up any number and type of body parts, such as 14 bodyparts including head, neck, shoulders, hips, etc., of each person,including adults and kids.

In one embodiment, ITL computation logic 211 may then be triggered tocompute torso length, L_(p,j), of each person, p, in the j^(th) view, asthe distance between the neck and the hip center as follows:

L _(p,j)=√{square root over ((N _(x) −H _(x))²+(N _(y) −H _(y))²)}

Where (N_(x), N_(y)) refers to pixel co-ordinates of the neck, while(H_(x), H_(y)) refers to pixel co-ordinates of the hip center. This isfurther illustrated with reference to FIG. 3D, where a person'simage-based model 360 shows computed image torso length 361, L_(p,j),hip center 363, (H_(x), H_(y)), and neck position 365, (N_(x), N_(y)).

As mentioned above, for example, a kid standing close to a camera, suchas camera 242A, and an adult standing away from the same camera 242A mayseem to have the same torso length, L_(p,j), due to the perspectiveprojection of camera 242A. To get the real length of the torso, L_(p,j)so that it is normalized with the corresponding depth as shown in graph370 of FIG. 3E, DNTL computation logic 213 is triggered to usesimilarity of triangles CPO 371 and CQM 373 to compute the depthnormalized torso length, L_(norm,p,j), as follows:

L _(norm,p,j) =D _(p,j) *L _(p,j)

Where D_(p,j) refers to the depth of the p^(th) human inferred from thedepth map in the j^(th) view.

In one embodiment, overall DNTL computation logic 215 may be used toestablish correspondence between people in multiple views, where supposethe p^(th) person is located in a subset of k views amongst a total of Jviews, then the overall DNTL, L_(norm,p) may be computed as the maximumof the DNTLs in those k views in which the considered person is present,as follows:

$L_{{norm},p} = {\max\limits_{k}{\left( L_{{norm},p,k} \right).}}$

Further, in one embodiment, classification logic 217 may be triggered touse L_(norm,p) to classify each person in the scene as an adult or a kidas follows:

${Person}_{p} = \left\{ \begin{matrix}{kid} & {L_{{norm},p} < T} \\{Adult} & {Otherwise}\end{matrix} \right.$

Where, the threshold, T, depends on the camera resolution and depthmetrics of one or more of cameras 242A, 242B, 242N.

As discussed above and throughout this document, age mechanism 110provides for a novel technique for using human torso length as theinvariant feature for characterizing and differentiating between adultsand kids, such as the use of human torso length and the depth forachieving adult-kid classification. Further, the novel technique useshuman pose estimation and depth sensors to facilitate the adult-kidclassification such that persons are first located through a face orperson detection and then clustered into spatially contiguous depthtiles for scale-invariant deep learning-based human pose estimation.

Capturing/sensing component(s) 231 may further include any number andtype of camera(s) 242A, 242B, 242N, such as depth-sensing cameras orcapturing devices (e.g., Intel® RealSense™ depth-sensing camera) thatare known for capturing still and/or video red-green-blue (RGB) and/orRGB-depth (RGB-D) images for media, such as personal media. Such images,having depth information, have been effectively used for variouscomputer vision and computational photography effects, such as (withoutlimitations) scene understanding, refocusing, composition,cinema-graphs, etc. Similarly, for example, displays may include anynumber and type of displays, such as integral displays, tensor displays,stereoscopic displays, etc., including (but not limited to) embedded orconnected display screens, display devices, projectors, etc.

Capturing/sensing component(s) 231 may further include one or more ofvibration components, tactile components, conductance elements,biometric sensors, chemical detectors, signal detectors,electroencephalography, functional near-infrared spectroscopy, wavedetectors, force sensors (e.g., accelerometers), illuminators,eye-tracking or gaze-tracking system, head-tracking system, etc., thatmay be used for capturing any amount and type of visual data, such asimages (e.g., photos, videos, movies, audio/video streams, etc.), andnon-visual data, such as audio streams or signals (e.g., sound, noise,vibration, ultrasound, etc.), radio waves (e.g., wireless signals, suchas wireless signals having data, metadata, signs, etc.), chemicalchanges or properties (e.g., humidity, body temperature, etc.),biometric readings (e.g., figure prints, etc.), brainwaves, braincirculation, environmental/weather conditions, maps, etc. It iscontemplated that “sensor” and “detector” may be referencedinterchangeably throughout this document. It is further contemplatedthat one or more capturing/sensing component(s) 231 may further includeone or more of supporting or supplemental devices for capturing and/orsensing of data, such as illuminators (e.g., IR illuminator), lightfixtures, generators, sound blockers, etc.

It is further contemplated that in one embodiment, capturing/sensingcomponent(s) 231 may further include any number and type of contextsensors (e.g., linear accelerometer) for sensing or detecting any numberand type of contexts (e.g., estimating horizon, linear acceleration,etc., relating to a mobile computing device, etc.). For example,capturing/sensing component(s) 231 may include any number and type ofsensors, such as (without limitations): accelerometers (e.g., linearaccelerometer to measure linear acceleration, etc.); inertial devices(e.g., inertial accelerometers, inertial gyroscopes,micro-electro-mechanical systems (MEMS) gyroscopes, inertial navigators,etc.); and gravity gradiometers to study and measure variations ingravitation acceleration due to gravity, etc.

Further, for example, capturing/sensing component(s) 231 may include(without limitations): audio/visual devices (e.g., cameras, microphones,speakers, etc.); context-aware sensors (e.g., temperature sensors,facial expression and feature measurement sensors working with one ormore cameras of audio/visual devices, environment sensors (such as tosense background colors, lights, etc.); biometric sensors (such as todetect fingerprints, etc.), calendar maintenance and reading device),etc.; global positioning system (GPS) sensors; resource requestor;and/or TEE logic. TEE logic may be employed separately or be part ofresource requestor and/or an I/O subsystem, etc. Capturing/sensingcomponent(s) 231 may further include voice recognition devices, photorecognition devices, facial and other body recognition components,voice-to-text conversion components, etc.

Similarly, output component(s) 233 may include dynamic tactile touchscreens having tactile effectors as an example of presentingvisualization of touch, where an embodiment of such may be ultrasonicgenerators that can send signals in space which, when reaching, forexample, human fingers can cause tactile sensation or like feeling onthe fingers. Further, for example and in one embodiment, outputcomponent(s) 233 may include (without limitation) one or more of lightsources, display devices and/or screens, audio speakers, tactilecomponents, conductance elements, bone conducting speakers, olfactory orsmell visual and/or non/visual presentation devices, haptic or touchvisual and/or non-visual presentation devices, animation displaydevices, biometric display devices, X-ray display devices,high-resolution displays, high-dynamic range displays, multi-viewdisplays, and head-mounted displays (HMDs) for at least one of virtualreality (VR) and augmented reality (AR), etc.

It is contemplated that embodiment are not limited to any particularnumber or type of use-case scenarios, architectural placements, orcomponent setups; however, for the sake of brevity and clarity,illustrations and descriptions are offered and discussed throughout thisdocument for exemplary purposes but that embodiments are not limited assuch. Further, throughout this document, “user” may refer to someonehaving access to one or more computing devices, such as computing device100, and may be referenced interchangeably with “person”, “individual”,“human”, “him”, “her”, “child”, “adult”, “viewer”, “player”, “gamer”,“developer”, programmer”, and/or the like.

Communication/compatibility logic 209 may be used to facilitate dynamiccommunication and compatibility between various components, networks,computing devices, database(s) 225, and/or communication medium(s) 230,etc., and any number and type of other computing devices (such aswearable computing devices, mobile computing devices, desktop computers,server computing devices, etc.), processing devices (e.g., centralprocessing unit (CPU), graphics processing unit (GPU), etc.),capturing/sensing components (e.g., non-visual data sensors/detectors,such as audio sensors, olfactory sensors, haptic sensors, signalsensors, vibration sensors, chemicals detectors, radio wave detectors,force sensors, weather/temperature sensors, body/biometric sensors,scanners, etc., and visual data sensors/detectors, such as cameras,etc.), user/context-awareness components and/oridentification/verification sensors/devices (such as biometricsensors/detectors, scanners, etc.), memory or storage devices, datasources, and/or database(s) (such as data storage devices, hard drives,solid-state drives, hard disks, memory cards or devices, memorycircuits, etc.), network(s) (e.g., Cloud network, Internet, Internet ofThings, intranet, cellular network, proximity networks, such asBluetooth, Bluetooth low energy (BLE), Bluetooth Smart, Wi-Fi proximity,Radio Frequency Identification, Near Field Communication, Body AreaNetwork, etc.), wireless or wired communications and relevant protocols(e.g., Wi-Fi®, WiMAX, Ethernet, etc.), connectivity and locationmanagement techniques, software applications/websites, (e.g., socialand/or business networking websites, business applications, games andother entertainment applications, etc.), programming languages, etc.,while ensuring compatibility with changing technologies, parameters,protocols, standards, etc.

Throughout this document, terms like “logic”, “component”, “module”,“framework”, “engine”, “tool”, “circuitry”, and/or the like, may bereferenced interchangeably and include, by way of example, software,hardware, and/or any combination of software and hardware, such asfirmware. In one example, “logic” may refer to or include a softwarecomponent that is capable of working with one or more of an operatingsystem, a graphics driver, etc., of a computing device, such ascomputing device 100. In another example, “logic” may refer to orinclude a hardware component that is capable of being physicallyinstalled along with or as part of one or more system hardware elements,such as an application processor, a graphics processor, etc., of acomputing device, such as computing device 100. In yet anotherembodiment, “logic” may refer to or include a firmware component that iscapable of being part of system firmware, such as firmware of anapplication processor or a graphics processor, etc., of a computingdevice, such as computing device 100.

Further, any use of a particular brand, word, term, phrase, name, and/oracronym, such as “adults”, “kids”, “adults-kids classification”,“depth”, “RGB”, “person detection”, “depth-based RGB tiling”, “RGB tilescaling”, “human 2D pose”, “image torso length”, “ITL”, “depthnormalized torso length”, “DNTL”, “overall DNTL”, “RealSense™ camera”,“real-time”, “automatic”, “dynamic”, “user interface”, “camera”,“sensor”, “microphone”, “display screen”, “speaker”, “verification”,“authentication”, “privacy”, “user”, “user profile”, “user preference”,“sender”, “receiver”, “personal device”, “smart device”, “mobilecomputer”, “wearable device”, “IoT device”, “proximity network”, “cloudnetwork”, “server computer”, etc., should not be read to limitembodiments to software or devices that carry that label in products orin literature external to this document.

It is contemplated that any number and type of components may be addedto and/or removed from age mechanism 110 to facilitate variousembodiments including adding, removing, and/or enhancing certainfeatures. For brevity, clarity, and ease of understanding of agemechanism 110, many of the standard and/or known components, such asthose of a computing device, are not shown or discussed here. It iscontemplated that embodiments, as described herein, are not limited toany particular technology, topology, system, architecture, and/orstandard and are dynamic enough to adopt and adapt to any futurechanges.

FIG. 3A illustrates torso foreshortening in multiple views according toone embodiment and as previously described with reference to FIG. 2. Forbrevity, many of the details previously discussed with reference toFIGS. 1-2 may not be discussed or repeated hereafter.

FIG. 3B illustrates a graph 320 showing DNTL of kids and adults in ascene obtained using single view cameras according to one embodiment andas previously described with reference to FIG. 2. For brevity, many ofthe details previously discussed with reference to FIGS. 1-3A may not bediscussed or repeated hereafter.

FIG. 3C illustrates a top view of a scene 340 containing adults and kidsas viewed by multiple pairs of RGB cameras 341, 342, 343, 344, 345 anddepth cameras 346, 347, 348, 349, 350 according to one embodiment and aspreviously described with reference to FIG. 2. For brevity, many of thedetails previously discussed with reference to FIGS. 1-3B may not bediscussed or repeated hereafter.

FIG. 3D illustrates torso length 361, neck 365, and hip center 363 on amodel 360 according to one embodiment and as previously described withreference to FIG. 2. For brevity, many of the details previouslydiscussed with reference to FIGS. 1-3C may not be discussed or repeatedhereafter.

FIG. 3E illustrates a graph 370 showing a camera-based DNTL computationusing ITL and depth according to one embodiment and as previouslydescribed with reference to FIG. 2. For brevity, many of the detailspreviously discussed with reference to FIGS. 1-3D may not be discussedor repeated hereafter.

FIG. 3F illustrates an unclassified scene 381 and a classified scene 383according to one embodiment. For brevity, many of the details previouslydiscussed with reference to FIGS. 1-3E may not be discussed or repeatedhereafter. As illustrated, unclassified scene 381 represents a raw sceneas captured by one or more cameras, such as cameras 242A, 242B, 242N ofFIG. 2, where unclassified scene 381 is shown to have persons ofdifferent ages, such as adults and children, but they are not yetclassified as such.

In one embodiment, using age mechanism 110 of FIG. 2, unclassified scene381 is processed into classified scene 383 such that persons shown inunclassified scene 381 are now classified as adults 385A, 385B, 385C,385D or kids 387A, 387B, 387C as discussed with reference to FIG. 2. Asillustrated, unclassified scene 381 shows a swimming pool and an areaaround the swimming pool having individuals of varying ages, rangingfrom kids to adults. Further, in the illustrated embodiment, agemechanism 110 and its components and processes as described with respectto FIG. 2 may be triggered to convert unclassified scene 381 intoclassified scene 383 by using, in one embodiment, human-pose (overlyingon people) and depth such that classified scene 383 identifies eachperson either as an adult 385A, 385B, 385C, 385D or a kid 387A, 387B,387C.

The application of the novel technique on a swimming pool video frame asshown in scenes 381, 383, as facilitated by age mechanism 110 of FIG. 2,allows for a clear distinction between the originally capturedunclassified scene 381 and the pose and classification results-basedclassification scene 383. It is contemplated that in some embodiments,better results may be obtained by altering the number and type of someof the components, such as using multi-view cameras as opposed tosingle-view cameras.

Further, since there are gender-based differences in the average heightof males and females, a gender-based threshold may be used for obtainingbetter results, such as by using a robust gender recognition techniqueover a wide range of arbitrary poses such that two threshold valuesincluding one for male torso lengths and one for female torso lengthsmay be set and applied to further improve the accuracy of adult-kidclassification.

It is further contemplated that various countries, states, regions,territories, cities, etc., may have different age requirements foradults or unsupervised minor and thus, embodiments are not limited toany age or age range for classification purposes. For example, 14 yearsis the acceptable required age to classify unsupervised minors in moststates in the United States; however, embodiments are not limited assuch and this age could be changed to another age, such as 13 or 16 or18, etc., or age range, such as 1-10 years for supervised youngchildren, 10-14 years for supervised older children, 14 years and abovefor unsupervised children and adults, and/or the like.

FIG. 4 illustrates a method 400 for classification of adults and kidsaccording to one embodiment. For brevity, many of the details previouslydiscussed with reference to FIGS. 1-3F may not be discussed or repeatedhereafter. Any processes or transactions may be performed by processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, etc.), software (such as instructions run on aprocessing device), or a combination thereof, as facilitated by agemechanism 110 of FIG. 1. Any processes or transactions associated withthis illustration may be illustrated or recited in linear sequences forbrevity and clarity in presentation; however, it is contemplated thatany number of them can be performed in parallel, asynchronously, or indifferent orders.

Method 400 starts at blocks 401 and 403 with inputs from depth cameraand RGB camera, respectively. At block 405, the RGB video/image input isfurther used for detection of persons. Further, RGB video is used forestimating multi-view person correspondence at block 419. Both the depthvideo and the person/facial detection information obtained from the RGBvideo are used for partitioning of person into depth-based tiles atblock 407. In one embodiment, scaling of the depth-based tiles isperformed at block 409, while subsequently, human 2D pose estimation isperformed at block 411.

In some embodiments, a camera system may be employed having an areaunder observation covered by multiple cameras with significant overlap.Further, it is contemplated that a person may be detected by more thanjust one camera, such as by any combination of two or more of cameras.Further, for example, identical persons may be located from multiplecamera views such that local depths (from each camera) and global depths(from a reference point) may be calculated. Since a single person may bedetected by more than just one camera, it is contemplated thatcorrespondingly, more than one torso length values may exist for thatperson as viewed through those cameras. Although torso length may beused as a feature to determine the age of persons, such as whether theyare adult or kids, it is contemplated that in some embodiments, torsolength may also be used for robustly compute other dimensions of humansacross various poses, actions, and/or the like.

In one embodiment, at block 413, image torso length is computed usingthe human 2D pose estimation, while at block 415, depth normalized torsolength is computed. At block 417, in one embodiment, for each person inthe scene, an overall DNTL is computed based on the computed DNTL andestimated multi-view person correspondence. At block 421, a decision ismade as to whether the overall DNTL is greater than or less than apredetermined threshold value, T, where this threshold may be a numberor a value that equals to or corresponds to the legal or acceptable agefor adults/kids or requirement for supervised/unsupervised kids, such as14 years being the legal and acceptable age for unsupervised kids, while18 years being the legal age for adults in most states in the UnitedStates and most countries around the world.

If the overall DNTL is determined to be greater than the thresholdvalue, the person is classified as an adult at block 423, while if theoverall DNTL is determined to be equal to or less than the thresholdvalue, then the person is classified as a kid at block 425. As furtherillustrated, certain processes, such as from block 401 through block 415and block 419, may be performed for each view or scene. Similarly,processes ranging from block 409 to 415 and block 419 may be performedfor each RGB tile, while processes of blocks 417-425 may be performedfor each person in the scene.

FIG. 5 illustrates a computing device 500 in accordance with oneimplementation. The illustrated computing device 500 may be same as orsimilar to computing device 100 of FIG. 1. The computing device 500houses a system board 502. The board 502 may include a number ofcomponents, including but not limited to a processor 504 and at leastone communication package 506. The communication package is coupled toone or more antennas 516. The processor 504 is physically andelectrically coupled to the board 502.

Depending on its applications, computing device 500 may include othercomponents that may or may not be physically and electrically coupled tothe board 502. These other components include, but are not limited to,volatile memory (e.g., DRAM) 508, non-volatile memory (e.g., ROM) 509,flash memory (not shown), a graphics processor 512, a digital signalprocessor (not shown), a crypto processor (not shown), a chipset 514, anantenna 516, a display 518 such as a touchscreen display, a touchscreencontroller 520, a battery 522, an audio codec (not shown), a video codec(not shown), a power amplifier 524, a global positioning system (GPS)device 526, a compass 528, an accelerometer (not shown), a gyroscope(not shown), a speaker 530, cameras 532, a microphone array 534, and amass storage device (such as hard disk drive) 510, compact disk (CD)(not shown), digital versatile disk (DVD) (not shown), and so forth).These components may be connected to the system board 502, mounted tothe system board, or combined with any of the other components.

The communication package 506 enables wireless and/or wiredcommunications for the transfer of data to and from the computing device500. The term “wireless” and its derivatives may be used to describecircuits, devices, systems, methods, techniques, communicationschannels, etc., that may communicate data through the use of modulatedelectromagnetic radiation through a non-solid medium. The term does notimply that the associated devices do not contain any wires, although insome embodiments they might not. The communication package 506 mayimplement any of a number of wireless or wired standards or protocols,including but not limited to Wi-Fi (IEEE 802.11 family), WiMAX (IEEE802.16 family), IEEE 802.20, long term evolution (LTE), Ev-DO, HSPA+,HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, Bluetooth, Ethernetderivatives thereof, as well as any other wireless and wired protocolsthat are designated as 3G, 4G, 5G, and beyond. The computing device 500may include a plurality of communication packages 506. For instance, afirst communication package 506 may be dedicated to shorter rangewireless communications such as Wi-Fi and Bluetooth and a secondcommunication package 506 may be dedicated to longer range wirelesscommunications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, andothers.

The cameras 532 including any depth sensors or proximity sensor arecoupled to an optional image processor 536 to perform conversions,analysis, noise reduction, comparisons, depth or distance analysis,image understanding and other processes as described herein. Theprocessor 504 is coupled to the image processor to drive the processwith interrupts, set parameters, and control operations of imageprocessor and the cameras. Image processing may instead be performed inthe processor 504, the graphics CPU 512, the cameras 532, or in anyother device.

In various implementations, the computing device 500 may be a laptop, anetbook, a notebook, an ultrabook, a smartphone, a tablet, a personaldigital assistant (PDA), an ultra mobile PC, a mobile phone, a desktopcomputer, a server, a set-top box, an entertainment control unit, adigital camera, a portable music player, or a digital video recorder.The computing device may be fixed, portable, or wearable. In furtherimplementations, the computing device 500 may be any other electronicdevice that processes data or records data for processing elsewhere.

Embodiments may be implemented using one or more memory chips,controllers, CPUs (Central Processing Unit), microchips or integratedcircuits interconnected using a motherboard, an application specificintegrated circuit (ASIC), and/or a field programmable gate array(FPGA). The term “logic” may include, by way of example, software orhardware and/or combinations of software and hardware.

References to “one embodiment”, “an embodiment”, “example embodiment”,“various embodiments”, etc., indicate that the embodiment(s) sodescribed may include particular features, structures, orcharacteristics, but not every embodiment necessarily includes theparticular features, structures, or characteristics. Further, someembodiments may have some, all, or none of the features described forother embodiments.

In the following description and claims, the term “coupled” along withits derivatives, may be used. “Coupled” is used to indicate that two ormore elements co-operate or interact with each other, but they may ormay not have intervening physical or electrical components between them.

As used in the claims, unless otherwise specified, the use of theordinal adjectives “first”, “second”, “third”, etc., to describe acommon element, merely indicate that different instances of likeelements are being referred to, and are not intended to imply that theelements so described must be in a given sequence, either temporally,spatially, in ranking, or in any other manner.

The drawings and the forgoing description give examples of embodiments.Those skilled in the art will appreciate that one or more of thedescribed elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.Moreover, the actions of any flow diagram need not be implemented in theorder shown; nor do all of the acts necessarily need to be performed.Also, those acts that are not dependent on other acts may be performedin parallel with the other acts. The scope of embodiments is by no meanslimited by these specific examples. Numerous variations, whetherexplicitly given in the specification or not, such as differences instructure, dimension, and use of material, are possible. The scope ofembodiments is at least as broad as given by the following claims.

Embodiments may be provided, for example, as a computer program productwhich may include one or more transitory or non-transitorymachine-readable storage media having stored thereon machine-executableinstructions that, when executed by one or more machines such as acomputer, network of computers, or other electronic devices, may resultin the one or more machines carrying out operations in accordance withembodiments described herein. A machine-readable medium may include, butis not limited to, floppy diskettes, optical disks, CD-ROMs (CompactDisc-Read Only Memories), and magneto-optical disks, ROMs, RAMs, EPROMs(Erasable Programmable Read Only Memories), EEPROMs (ElectricallyErasable Programmable Read Only Memories), magnetic or optical cards,flash memory, or other type of media/machine-readable medium suitablefor storing machine-executable instructions.

FIG. 6 illustrates an embodiment of a computing environment 600 capableof supporting the operations discussed above. The modules and systemscan be implemented in a variety of different hardware architectures andform factors including that shown in FIG. 5.

The Command Execution Module 601 includes a central processing unit tocache and execute commands and to distribute tasks among the othermodules and systems shown. It may include an instruction stack, a cachememory to store intermediate and final results, and mass memory to storeapplications and operating systems. The Command Execution Module mayalso serve as a central coordination and task allocation unit for thesystem.

The Screen Rendering Module 621 draws objects on the one or moremultiple screens for the user to see. It can be adapted to receive thedata from the Virtual Object Behavior Module 604, described below, andto render the virtual object and any other objects and forces on theappropriate screen or screens. Thus, the data from the Virtual ObjectBehavior Module would determine the position and dynamics of the virtualobject and associated gestures, forces and objects, for example, and theScreen Rendering Module would depict the virtual object and associatedobjects and environment on a screen, accordingly. The Screen RenderingModule could further be adapted to receive data from the Adjacent ScreenPerspective Module 607, described below, to either depict a targetlanding area for the virtual object if the virtual object could be movedto the display of the device with which the Adjacent Screen PerspectiveModule is associated. Thus, for example, if the virtual object is beingmoved from a main screen to an auxiliary screen, the Adjacent ScreenPerspective Module 2 could send data to the Screen Rendering Module tosuggest, for example in shadow form, one or more target landing areasfor the virtual object on that track to a user's hand movements or eyemovements.

The Object and Gesture Recognition Module 622 may be adapted torecognize and track hand and arm gestures of a user. Such a module maybe used to recognize hands, fingers, finger gestures, hand movements anda location of hands relative to displays. For example, the Object andGesture Recognition Module could for example determine that a user madea body part gesture to drop or throw a virtual object onto one or theother of the multiple screens, or that the user made a body part gestureto move the virtual object to a bezel of one or the other of themultiple screens. The Object and Gesture Recognition System may becoupled to a camera or camera array, a microphone or microphone array, atouch screen or touch surface, or a pointing device, or some combinationof these items, to detect gestures and commands from the user.

The touch screen or touch surface of the Object and Gesture RecognitionSystem may include a touch screen sensor. Data from the sensor may befed to hardware, software, firmware or a combination of the same to mapthe touch gesture of a user's hand on the screen or surface to acorresponding dynamic behavior of a virtual object. The sensor date maybe used to momentum and inertia factors to allow a variety of momentumbehavior for a virtual object based on input from the user's hand, suchas a swipe rate of a user's finger relative to the screen. Pinchinggestures may be interpreted as a command to lift a virtual object fromthe display screen, or to begin generating a virtual binding associatedwith the virtual object or to zoom in or out on a display. Similarcommands may be generated by the Object and Gesture Recognition Systemusing one or more cameras without the benefit of a touch surface.

The Direction of Attention Module 623 may be equipped with cameras orother sensors to track the position or orientation of a user's face orhands. When a gesture or voice command is issued, the system candetermine the appropriate screen for the gesture. In one example, acamera is mounted near each display to detect whether the user is facingthat display. If so, then the direction of attention module informationis provided to the Object and Gesture Recognition Module 622 to ensurethat the gestures or commands are associated with the appropriatelibrary for the active display. Similarly, if the user is looking awayfrom all of the screens, then commands can be ignored.

The Device Proximity Detection Module 625 can use proximity sensors,compasses, GPS (global positioning system) receivers, personal areanetwork radios, and other types of sensors, together with triangulationand other techniques to determine the proximity of other devices. Once anearby device is detected, it can be registered to the system and itstype can be determined as an input device or a display device or both.For an input device, received data may then be applied to the ObjectGesture and Recognition Module 622. For a display device, it may beconsidered by the Adjacent Screen Perspective Module 607.

The Virtual Object Behavior Module 604 is adapted to receive input fromthe Object Velocity and Direction Module, and to apply such input to avirtual object being shown in the display. Thus, for example, the Objectand Gesture Recognition System would interpret a user gesture and bymapping the captured movements of a user's hand to recognized movements,the Virtual Object Tracker Module would associate the virtual object'sposition and movements to the movements as recognized by Object andGesture Recognition System, the Object and Velocity and Direction Modulewould capture the dynamics of the virtual object's movements, and theVirtual Object Behavior Module would receive the input from the Objectand Velocity and Direction Module to generate data that would direct themovements of the virtual object to correspond to the input from theObject and Velocity and Direction Module.

The Virtual Object Tracker Module 606 on the other hand may be adaptedto track where a virtual object should be located in three-dimensionalspace in a vicinity of a display, and which body part of the user isholding the virtual object, based on input from the Object and GestureRecognition Module. The Virtual Object Tracker Module 606 may forexample track a virtual object as it moves across and between screensand track which body part of the user is holding that virtual object.Tracking the body part that is holding the virtual object allows acontinuous awareness of the body part's air movements, and thus aneventual awareness as to whether the virtual object has been releasedonto one or more screens.

The Gesture to View and Screen Synchronization Module 608, receives theselection of the view and screen or both from the Direction of AttentionModule 623 and, in some cases, voice commands to determine which view isthe active view and which screen is the active screen. It then causesthe relevant gesture library to be loaded for the Object and GestureRecognition Module 622. Various views of an application on one or morescreens can be associated with alternative gesture libraries or a set ofgesture templates for a given view. As an example, in FIG. 1A, apinch-release gesture launches a torpedo, but in FIG. 1B, the samegesture launches a depth charge.

The Adjacent Screen Perspective Module 607, which may include or becoupled to the Device Proximity Detection Module 625, may be adapted todetermine an angle and position of one display relative to anotherdisplay. A projected display includes, for example, an image projectedonto a wall or screen. The ability to detect a proximity of a nearbyscreen and a corresponding angle or orientation of a display projectedtherefrom may for example be accomplished with either an infraredemitter and receiver, or electromagnetic or photo-detection sensingcapability. For technologies that allow projected displays with touchinput, the incoming video can be analyzed to determine the position of aprojected display and to correct for the distortion caused by displayingat an angle. An accelerometer, magnetometer, compass, or camera can beused to determine the angle at which a device is being held whileinfrared emitters and cameras could allow the orientation of the screendevice to be determined in relation to the sensors on an adjacentdevice. The Adjacent Screen Perspective Module 607 may, in this way,determine coordinates of an adjacent screen relative to its own screencoordinates. Thus, the Adjacent Screen Perspective Module may determinewhich devices are in proximity to each other, and further potentialtargets for moving one or more virtual objects across screens. TheAdjacent Screen Perspective Module may further allow the position of thescreens to be correlated to a model of three-dimensional spacerepresenting all of the existing objects and virtual objects.

The Object and Velocity and Direction Module 603 may be adapted toestimate the dynamics of a virtual object being moved, such as itstrajectory, velocity (whether linear or angular), momentum (whetherlinear or angular), etc. by receiving input from the Virtual ObjectTracker Module. The Object and Velocity and Direction Module may furtherbe adapted to estimate dynamics of any physics forces, by for exampleestimating the acceleration, deflection, degree of stretching of avirtual binding, etc. and the dynamic behavior of a virtual object oncereleased by a user's body part. The Object and Velocity and DirectionModule may also use image motion, size and angle changes to estimate thevelocity of objects, such as the velocity of hands and fingers

The Momentum and Inertia Module 602 can use image motion, image size,and angle changes of objects in the image plane or in athree-dimensional space to estimate the velocity and direction ofobjects in the space or on a display. The Momentum and Inertia Module iscoupled to the Object and Gesture Recognition Module 622 to estimate thevelocity of gestures performed by hands, fingers, and other body partsand then to apply those estimates to determine momentum and velocitiesto virtual objects that are to be affected by the gesture.

The 3D Image Interaction and Effects Module 605 tracks user interactionwith 3D images that appear to extend out of one or more screens. Theinfluence of objects in the z-axis (towards and away from the plane ofthe screen) can be calculated together with the relative influence ofthese objects upon each other. For example, an object thrown by a usergesture can be influenced by 3D objects in the foreground before thevirtual object arrives at the plane of the screen. These objects maychange the direction or velocity of the projectile or destroy itentirely. The object can be rendered by the 3D Image Interaction andEffects Module in the foreground on one or more of the displays. Asillustrated, various components, such as components 601, 602, 603, 604,605. 606, 607, and 608 are connected via an interconnect or a bus, suchas bus 609.

The following clauses and/or examples pertain to further embodiments orexamples. Specifics in the examples may be used anywhere in one or moreembodiments. The various features of the different embodiments orexamples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system forfacilitating hybrid communication according to embodiments and examplesdescribed herein.

Some embodiments pertain to Example 1 that includes an apparatus tofacilitate age classification of humans using image depth and humanpose, the apparatus comprising: detection and capturing logic tofacilitate one or more cameras to capture a video stream of a scenehaving persons; overall depth normalized torso length (overall DNTL)computation logic to compute overall-depth torso lengths of the personsbased on depth torso lengths of the persons, wherein the overall DNTLcomputation logic is further to compare the overall-depth torso lengthswith a predetermined threshold value representing a separation agebetween adults and children; and classification logic to classify afirst set of the persons as adults if a first set of the overall-depthtorso lengths associated with the first set of persons is greater thanthe threshold value.

Example 2 includes the subject matter of Example 1, wherein theclassification logic is further to classify a second set of the personsas children if a second set of the overall-depth torso lengthsassociated with the second set of persons is equal to or less than thethreshold value.

Example 3 includes the subject matter of Examples 1-2, furthercomprising depth-based tiling logic to approximate depth values of thepersons and partition the persons into depth-based tiles using the depthvalues, wherein the detection and capturing logic is further to detectthe persons in the scene.

Example 4 includes the subject matter of Examples 1-3, furthercomprising: tile scaling logic to facilitate a deep neural network toscale the depth-based tiles, where scaling of the depth-based tilesincludes red green blue (RGB) tile scaling; and pose estimation logic tolocate pixel locations of one or more body parts of each person todetect placement and length of each of the one or more body parts,wherein the one or more body parts include one or more of heads, necks,shoulders, and hips.

Example 5 includes the subject matter of Examples 1-4, furthercomprising image torso length (ITL) computation logic to compute imagetorso length of the persons, wherein an image torso length of a personrepresents a distance between a neck and a hip center of the person.

Example 6 includes the subject matter of Examples 1-5, furthercomprising DNTL computation logic to convert the image torso lengthsinto the depth torso lengths of the persons based on normalized depthsassociated with the persons, wherein a depth torso length represents areal torso length, and wherein each normalized depth is inferred from aposition of each person with respect to the one or more cameras.

Example 7 includes the subject matter of Examples 1-6, wherein theapparatus comprises one or more processors including a graphicsprocessor co-located with an application processor on a commonsemiconductor package.

Some embodiments pertain to Example 8 that includes a methodfacilitating age classification of humans using image depth and humanpose, the method comprising: facilitating, by one or more cameras of acomputing device, capturing of a video stream of a scene having persons;computing overall-depth torso lengths of the persons based on depthtorso lengths of the persons; comparing the overall-depth torso lengthswith a predetermined threshold value representing a separation agebetween adults and children; and classifying a first set of the personsas adults if a first set of the overall-depth torso lengths associatedwith the first set of persons is greater than the threshold value.

Example 9 includes the subject matter of Example 8, further comprisingclassifying a second set of the persons as children if a second set ofthe overall-depth torso lengths associated with the second set ofpersons is equal to or less than the threshold value.

Example 10 includes the subject matter of Examples 8-9, furthercomprising: approximating depth values of the persons and partition thepersons into depth-based tiles using the depth values; and detecting thepersons in the scene.

Example 11 includes the subject matter of Examples 8-10, furthercomprising: facilitating a deep neural network to scale the depth-basedtiles, where scaling of the depth-based tiles includes red green blue(RGB) tile scaling; and locating pixel locations of one or more bodyparts of each person to detect placement and length of each of the oneor more body parts, wherein the one or more body parts include one ormore of heads, necks, shoulders, and hips.

Example 12 includes the subject matter of Examples 8-11, furthercomprising computing image torso length of the persons, wherein an imagetorso length of a person represents a distance between a neck and a hipcenter of the person.

Example 13 includes the subject matter of Examples 8-12, furthercomprising converting the image torso lengths into the depth torsolengths of the persons based on normalized depths associated with thepersons, wherein a depth torso length represents a real torso length,and wherein each normalized depth is inferred from a position of eachperson with respect to the one or more cameras.

Example 14 includes the subject matter of Examples 8-13, wherein thecomputing device comprises one or more processors including a graphicsprocessor co-located with an application processor on a commonsemiconductor package.

Some embodiments pertain to Example 15 that includes a data processingsystem having a processing device coupled to a memory device, the dataprocessing system having a camera system including one or more camerasto capture a video stream of a scene having persons, and the processingdevice to perform operations comprising: facilitating, by one or morecameras of a computing device, capturing of a video stream of a scenehaving persons; computing overall-depth torso lengths of the personsbased on depth torso lengths of the persons; comparing the overall-depthtorso lengths with a predetermined threshold value representing aseparation age between adults and children; and classifying a first setof the persons as adults if a first set of the overall-depth torsolengths associated with the first set of persons is greater than thethreshold value.

Example 16 includes the subject matter of Example 15, further comprisingclassifying a second set of the persons as children if a second set ofthe overall-depth torso lengths associated with the second set ofpersons is equal to or less than the threshold value.

Example 17 includes the subject matter of Examples 15-16, furthercomprising: approximating depth values of the persons and partition thepersons into depth-based tiles using the depth values; and detecting thepersons in the scene.

Example 18 includes the subject matter of Examples 15-17, furthercomprising: facilitating a deep neural network to scale the depth-basedtiles, where scaling of the depth-based tiles includes red green blue(RGB) tile scaling; and locating pixel locations of one or more bodyparts of each person to detect placement and length of each of the oneor more body parts, wherein the one or more body parts include one ormore of heads, necks, shoulders, and hips.

Example 19 includes the subject matter of Examples 15-18, furthercomprising computing image torso length of the persons, wherein an imagetorso length of a person represents a distance between a neck and a hipcenter of the person.

Example 20 includes the subject matter of Examples 15-19, furthercomprising converting the image torso lengths into the depth torsolengths of the persons based on normalized depths associated with thepersons, wherein a depth torso length represents a real torso length,and wherein each normalized depth is inferred from a position of eachperson with respect to the one or more cameras.

Example 21 includes the subject matter of Examples 15-20, wherein thecomputing device comprises one or more processors including a graphicsprocessor co-located with an application processor on a commonsemiconductor package.

Some embodiments pertain to Example 22 that includes an apparatus tofacilitate simultaneous recognition and processing of multiple speechesfrom multiple users, the apparatus comprising: means for facilitating,by one or more cameras, capturing of a video stream of a scene havingpersons; means for computing overall-depth torso lengths of the personsbased on depth torso lengths of the persons; means for comparing theoverall-depth torso lengths with a predetermined threshold valuerepresenting a separation age between adults and children; and means forclassifying a first set of the persons as adults if a first set of theoverall-depth torso lengths associated with the first set of persons isgreater than the threshold value.

Example 23 includes the subject matter of Example 22, further comprisingmeans for classifying a second set of the persons as children if asecond set of the overall-depth torso lengths associated with the secondset of persons is equal to or less than the threshold value.

Example 24 includes the subject matter of Examples 22-23, furthercomprising: means for approximating depth values of the persons andpartition the persons into depth-based tiles using the depth values; anddetecting the persons in the scene.

Example 25 includes the subject matter of Examples 22-24, furthercomprising: means for facilitating a deep neural network to scale thedepth-based tiles, where scaling of the depth-based tiles includes redgreen blue (RGB) tile scaling; and means for locating pixel locations ofone or more body parts of each person to detect placement and length ofeach of the one or more body parts, wherein the one or more body partsinclude one or more of heads, necks, shoulders, and hips.

Example 26 includes the subject matter of Examples 22-25, furthercomprising means for computing image torso length of the persons,wherein an image torso length of a person represents a distance betweena neck and a hip center of the person.

Example 27 includes the subject matter of Examples 22-26, furthercomprising means for converting the image torso lengths into the depthtorso lengths of the persons based on normalized depths associated withthe persons, wherein a depth torso length represents a real torsolength, and wherein each normalized depth is inferred from a position ofeach person with respect to the one or more cameras.

Example 28 includes the subject matter of Examples 22-27, wherein theapparatus comprises one or more processors including a graphicsprocessor co-located with an application processor on a commonsemiconductor package.

Example 28 includes the subject matter of Examples 22-25, wherein theapparatus comprises one or more processors including a graphicsprocessor co-located with an application processor on a commonsemiconductor package.

Example 29 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method asclaimed in any of claims or examples 8-14.

Example 30 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method as claimed in any of claims or examples8-14.

Example 31 includes a system comprising a mechanism to implement orperform a method as claimed in any of claims or examples 8-14.

Example 32 includes an apparatus comprising means for performing amethod as claimed in any of claims or examples 8-14.

Example 33 includes a computing device arranged to implement or performa method as claimed in any of claims or examples 8-14.

Example 34 includes a communications device arranged to implement orperform a method as claimed in any of claims or examples 8-14.

Example 35 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method or realize an apparatus as claimed in anypreceding claims.

Example 36 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method orrealize an apparatus as claimed in any preceding claims.

Example 37 includes a system comprising a mechanism to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

Example 38 includes an apparatus comprising means to perform a method asclaimed in any preceding claims.

Example 39 includes a computing device arranged to implement or performa method or realize an apparatus as claimed in any preceding claims.

Example 40 includes a communications device arranged to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

The drawings and the forgoing description give examples of embodiments.Those skilled in the art will appreciate that one or more of thedescribed elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.Moreover, the actions of any flow diagram need not be implemented in theorder shown; nor do all of the acts necessarily need to be performed.Also, those acts that are not dependent on other acts may be performedin parallel with the other acts. The scope of embodiments is by no meanslimited by these specific examples. Numerous variations, whetherexplicitly given in the specification or not, such as differences instructure, dimension, and use of material, are possible. The scope ofembodiments is at least as broad as given by the following claims.

What is claimed is:
 1. An apparatus comprising: detection and capturinglogic to facilitate one or more cameras to capture a video stream of ascene having persons; overall depth normalized torso length (overallDNTL) computation logic to compute overall-depth torso lengths of thepersons based on depth torso lengths of the persons, wherein the overallDNTL computation logic is further to compare the overall-depth torsolengths with a predetermined threshold value representing a separationage between adults and children; and classification logic to classify afirst set of the persons as adults if a first set of the overall-depthtorso lengths associated with the first set of persons is greater thanthe threshold value.
 2. The apparatus of claim 1, wherein theclassification logic is further to classify a second set of the personsas children if a second set of the overall-depth torso lengthsassociated with the second set of persons is equal to or less than thethreshold value.
 3. The apparatus of claim 1, further comprisingdepth-based tiling logic to approximate depth values of the persons andpartition the persons into depth-based tiles using the depth values,wherein the detection and capturing logic is further to detect thepersons in the scene.
 4. The apparatus of claim 1, further comprising:tile scaling logic to facilitate a deep neural network to scale thedepth-based tiles, where scaling of the depth-based tiles includes redgreen blue (RGB) tile scaling; and pose estimation logic to locate pixellocations of one or more body parts of each person to detect placementand length of each of the one or more body parts, wherein the one ormore body parts include one or more of heads, necks, shoulders, andhips.
 5. The apparatus of claim 1, further comprising image torso length(ITL) computation logic to compute image torso length of the persons,wherein an image torso length of a person represents a distance betweena neck and a hip center of the person.
 6. The apparatus of claim 1,further comprising DNTL computation logic to convert the image torsolengths into the depth torso lengths of the persons based on normalizeddepths associated with the persons, wherein a depth torso lengthrepresents a real torso length, and wherein each normalized depth isinferred from a position of each person with respect to the one or morecameras.
 7. The apparatus of claim 1, wherein the apparatus comprisesone or more processors including a graphics processor co-located with anapplication processor on a common semiconductor package.
 8. A methodcomprising: facilitating, by one or more cameras of a computing device,capturing of a video stream of a scene having persons; computingoverall-depth torso lengths of the persons based on depth torso lengthsof the persons; comparing the overall-depth torso lengths with apredetermined threshold value representing a separation age betweenadults and children; and classifying a first set of the persons asadults if a first set of the overall-depth torso lengths associated withthe first set of persons is greater than the threshold value.
 9. Themethod of claim 8, further comprising classifying a second set of thepersons as children if a second set of the overall-depth torso lengthsassociated with the second set of persons is equal to or less than thethreshold value.
 10. The method of claim 8, further comprising:approximating depth values of the persons and partition the persons intodepth-based tiles using the depth values; and detecting the persons inthe scene.
 11. The method of claim 8, further comprising: facilitating adeep neural network to scale the depth-based tiles, where scaling of thedepth-based tiles includes red green blue (RGB) tile scaling; andlocating pixel locations of one or more body parts of each person todetect placement and length of each of the one or more body parts,wherein the one or more body parts include one or more of heads, necks,shoulders, and hips.
 12. The method of claim 8, further comprisingcomputing image torso length of the persons, wherein an image torsolength of a person represents a distance between a neck and a hip centerof the person.
 13. The method of claim 8, further comprising convertingthe image torso lengths into the depth torso lengths of the personsbased on normalized depths associated with the persons, wherein a depthtorso length represents a real torso length, and wherein each normalizeddepth is inferred from a position of each person with respect to the oneor more cameras.
 14. The method of claim 8, wherein the computing devicecomprises one or more processors including a graphics processorco-located with an application processor on a common semiconductorpackage.
 15. At least one machine-readable medium comprisinginstructions which, when executed by a computing device, cause thecomputing device to perform operations comprising: facilitating, by oneor more cameras of the computing device, capturing of a video stream ofa scene having persons; computing overall-depth torso lengths of thepersons based on depth torso lengths of the persons; comparing theoverall-depth torso lengths with a predetermined threshold valuerepresenting a separation age between adults and children; andclassifying a first set of the persons as adults if a first set of theoverall-depth torso lengths associated with the first set of persons isgreater than the threshold value.
 16. The machine-readable medium ofclaim 15, further comprising classifying a second set of the persons aschildren if a second set of the overall-depth torso lengths associatedwith the second set of persons is equal to or less than the thresholdvalue.
 17. The machine-readable medium of claim 15, further comprising:approximating depth values of the persons and partition the persons intodepth-based tiles using the depth values; and detecting the persons inthe scene.
 18. The machine-readable medium of claim 15, furthercomprising: facilitating a deep neural network to scale the depth-basedtiles, where scaling of the depth-based tiles includes red green blue(RGB) tile scaling; and locating pixel locations of one or more bodyparts of each person to detect placement and length of each of the oneor more body parts, wherein the one or more body parts include one ormore of heads, necks, shoulders, and hips.
 19. The machine-readablemedium of claim 15, further comprising computing image torso length ofthe persons, wherein an image torso length of a person represents adistance between a neck and a hip center of the person.
 20. Themachine-readable medium of claim 15, further comprising converting theimage torso lengths into the depth torso lengths of the persons based onnormalized depths associated with the persons, wherein a depth torsolength represents a real torso length, and wherein each normalized depthis inferred from a position of each person with respect to the one ormore cameras, wherein the computing device comprises one or moreprocessors including a graphics processor co-located with an applicationprocessor on a common semiconductor package.
 21. A system comprising: adata processing system having a processing device coupled to a memorydevice, the data processing system having a camera system including oneor more cameras to capture a video stream of a scene having persons, andthe processing device to perform operations comprising: computingoverall-depth torso lengths of the persons based on depth torso lengthsof the persons; comparing the overall-depth torso lengths with apredetermined threshold value representing a separation age betweenadults and children; and classifying a first set of the persons asadults if a first set of the overall-depth torso lengths associated withthe first set of persons is greater than the threshold value.
 22. Thesystem of claim 21, wherein the operations further comprise: classifyinga second set of the persons as children if a second set of theoverall-depth torso lengths associated with the second set of persons isequal to or less than the threshold value; approximating depth values ofthe persons and partition the persons into depth-based tiles using thedepth values; and detecting the persons in the scene.
 23. The system ofclaim 21, wherein the operations are further to: facilitating a deepneural network to scale the depth-based tiles, where scaling of thedepth-based tiles includes red green blue (RGB) tile scaling; andlocating pixel locations of one or more body parts of each person todetect placement and length of each of the one or more body parts,wherein the one or more body parts include one or more of heads, necks,shoulders, and hips.
 24. The system of claim 21, wherein the operationsare further to: computing image torso length of the persons, wherein animage torso length of a person represents a distance between a neck anda hip center of the person; and converting the image torso lengths intothe depth torso lengths of the persons based on normalized depthsassociated with the persons, wherein a depth torso length represents areal torso length, and wherein each normalized depth is inferred from aposition of each person with respect to the one or more cameras.
 25. Thesystem of claim 21, wherein the processing device comprises a graphicsprocessor co-located with an application processor on a commonsemiconductor package.